@article{fieberg_jenkins_2005, title={Assessing uncertainty in ecological systems using global sensitivity analyses: a case example of simulated wolf reintroduction effects on elk}, volume={187}, ISSN={["0304-3800"]}, DOI={10.1016/j.ecolmodel.2005.01.042}, abstractNote={Often landmark conservation decisions are made despite an incomplete knowledge of system behavior and inexact predictions of how complex ecosystems will respond to management actions. For example, predicting the feasibility and likely effects of restoring top-level carnivores such as the gray wolf (Canis lupus) to North American wilderness areas is hampered by incomplete knowledge of the predator-prey system processes and properties. In such cases, global sensitivity measures, such as Sobol’ indices, allow one to quantify the effect of these uncertainties on model predictions. Sobol’ indices are calculated by decomposing the variance in model predictions (due to parameter uncertainty) into main effects of model parameters and their higher order interactions. Model parameters with large sensitivity indices can then be identified for further study in order to improve predictive capabilities. Here, we illustrate the use of Sobol’ sensitivity indices to examine the effect of parameter uncertainty on the predicted decline of elk (Cervus elaphus) population sizes following a hypothetical reintroduction of wolves to Olympic National Park, Washington, USA. The strength of density dependence acting on survival of adult elk and magnitude of predation were the most influential factors controlling elk population size following a simulated wolf reintroduction. In particular, the form of density dependence in natural survival rates and the per-capita predation rate together accounted for over 90% of variation in simulated elk population trends. Additional research on wolf predation rates on elk and natural compensations in prey populations is needed to reliably predict the outcome of predator–prey system behavior following wolf reintroductions.}, number={2-3}, journal={ECOLOGICAL MODELLING}, author={Fieberg, J and Jenkins, KJ}, year={2005}, month={Sep}, pages={259–280} }
 @article{ellner_fieberg_ludwig_wilcox_2002, title={Precision of population viability analysis}, volume={16}, ISSN={["1523-1739"]}, DOI={10.1046/j.1523-1739.2002.00553.x}, abstractNote={Although population viability analysis (PVA) is widely used in setting conservation policy, there is disagreement about the usefulness of this method. Objections have been raised concerning the precision of predictions in view of the short time series of data available and the sensitivity of estimates of extinction risk to estimated parameters (Hamilton & Moller 1995; Taylor 1995; Groom & Pascual 1998; Ludwig 1999). Beissinger and Westphal (1998) reviewed the use of demographic models for endangered-species management. They pointed out that poor data cause difficulties in parameter estimation, which in turn lead to unreliable estimates of extinction risk. There are additional}, number={1}, journal={CONSERVATION BIOLOGY}, author={Ellner, SP and Fieberg, J and Ludwig, D and Wilcox, C}, year={2002}, month={Feb}, pages={258–261} }
 @misc{fieberg_ellner_2001, title={Stochastic matrix models for conservation and management: a comparative review of methods}, volume={4}, ISSN={["1461-0248"]}, DOI={10.1046/j.1461-0248.2001.00202.x}, abstractNote={Stochastic matrix models are frequently used by conservation biologists to measure the viability of species and to explore various management actions. Models are typically parameterized using two or more sets of estimated transition rates between age/size/stage classes. While standard methods exist for analyzing a single set of transition rates, a variety of methods have been employed to analyze multiple sets of transition rates. We review applications of stochastic matrix models to problems in conservation and use simulation studies to compare the performance of different analytic methods currently in use. We find that model conclusions are likely to be robust to the choice of parametric distribution used to model vital rate fluctuations over time. However, conclusions can be highly sensitive to the within‐year correlation structure among vital rates, and therefore we suggest using analytical methods that provide a means of conducting a sensitivity analysis with respect to correlation parameters. Our simulation results also suggest that the precision of population viability estimates can be improved by using matrix models that incorporate environmental covariates in conjunction with experiments to estimate transition rates under a range of environmental conditions.}, number={3}, journal={ECOLOGY LETTERS}, author={Fieberg, J and Ellner, SP}, year={2001}, month={May}, pages={244–266} }
 @article{fieberg_ellner_2000, title={When is it meaningful to estimate an extinction probability?}, volume={81}, number={7}, journal={Ecology (Brooklyn, New York, N.Y.)}, author={Fieberg, J. and Ellner, S. P.}, year={2000}, pages={2040–2047} }